14 citations · 67 across the 56 of their papers we have counts for
12 papers · 1 filter
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Huanjin Yao, Jiaxing Huang, Wenhao Wu +8
In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this…
Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation
Wenda Li, Kaixuan Chen, Shunyu Liu +3
Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computationa…
Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Yuwen Wang, Shunyu Liu, Tongya Zheng +2
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representati…
Odyssey: Empowering Minecraft Agents with Open-World Skills
Shunyu Liu, Yaoru Li, Kongcheng Zhang +5
Recent studies have delved into constructing generalist agents for open-world environments like Minecraft. Despite the encouraging results, existing efforts mainly focus on solving…
Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
Feiyang Xu, Shunyu Liu, Yunpeng Qing +3
Active Voltage Control (AVC) on the Power Distribution Networks (PDNs) aims to stabilize the voltage levels to ensure efficient and reliable operation of power systems. With the in…
Improving Adversarial Robustness via Feature Pattern Consistency Constraint
Jiacong Hu, Jingwen Ye, Zunlei Feng +5
Convolutional Neural Networks (CNNs) are well-known for their vulnerability to adversarial attacks, posing significant security concerns. In response to these threats, various defe…
Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
Shunyu Liu, Wei Luo, Yanzhen Zhou +5
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schem…
COLA: Cross-city Mobility Transformer for Human Trajectory Simulation
Yu Wang, Tongya Zheng, Yuxuan Liang +2
Human trajectory data produced by daily mobile devices has proven its usefulness in various substantial fields such as urban planning and epidemic prevention. In terms of the indiv…
Simple Graph Condensation
Zhenbang Xiao, Yu Wang, Shunyu Liu +3
The burdensome training costs on large-scale graphs have aroused significant interest in graph condensation, which involves tuning Graph Neural Networks (GNNs) on a small condensed…
A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective
Yunpeng Qing, Shunyu liu, Jingyuan Cong +3
Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with…
Disentangled Condensation for Large-scale Graphs
Zhenbang Xiao, Yu Wang, Shunyu Liu +4
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the orig…
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Kaixuan Chen, Wei Luo, Shunyu Liu +6
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power f…